Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations446014
Missing cells1163037
Missing cells (%)20.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.3 MiB
Average record size in memory90.0 B

Variable types

DateTime1
Numeric9
Categorical2
Text1

Alerts

Estación has a high cardinality: 91 distinct values High cardinality
CO (mg/m3) is highly overall correlated with NO (ug/m3) and 4 other fieldsHigh correlation
Estación is highly overall correlated with Latitud and 2 other fieldsHigh correlation
Latitud is highly overall correlated with Estación and 1 other fieldsHigh correlation
Longitud is highly overall correlated with Estación and 1 other fieldsHigh correlation
NO (ug/m3) is highly overall correlated with CO (mg/m3) and 4 other fieldsHigh correlation
NO2 (ug/m3) is highly overall correlated with CO (mg/m3) and 4 other fieldsHigh correlation
PM10 (ug/m3) is highly overall correlated with CO (mg/m3) and 3 other fieldsHigh correlation
PM25 (ug/m3) is highly overall correlated with CO (mg/m3) and 3 other fieldsHigh correlation
Provincia is highly overall correlated with Estación and 2 other fieldsHigh correlation
SO2 (ug/m3) is highly overall correlated with CO (mg/m3) and 2 other fieldsHigh correlation
CO (mg/m3) has 344856 (77.3%) missing values Missing
NO (ug/m3) has 30984 (6.9%) missing values Missing
NO2 (ug/m3) has 32517 (7.3%) missing values Missing
O3 (ug/m3) has 170600 (38.2%) missing values Missing
PM10 (ug/m3) has 101435 (22.7%) missing values Missing
PM25 (ug/m3) has 392230 (87.9%) missing values Missing
SO2 (ug/m3) has 89737 (20.1%) missing values Missing

Reproduction

Analysis started2025-01-23 20:06:31.165556
Analysis finished2025-01-23 20:06:39.799872
Duration8.63 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Fecha
Date

Distinct8766
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Minimum1997-01-01 00:00:00
Maximum2020-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-23T20:06:39.840074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:39.904862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CO (mg/m3)
Real number (ℝ)

High correlation  Missing 

Distinct100
Distinct (%)0.1%
Missing344856
Missing (%)77.3%
Infinite0
Infinite (%)0.0%
Mean0.85462445
Minimum0
Maximum25.1
Zeros89
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-01-23T20:06:39.967957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.3
median0.7
Q31.1
95-th percentile2.3
Maximum25.1
Range25.1
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.78522551
Coefficient of variation (CV)0.91879598
Kurtosis23.281004
Mean0.85462445
Median Absolute Deviation (MAD)0.4
Skewness3.0068936
Sum86452.1
Variance0.6165791
MonotonicityNot monotonic
2025-01-23T20:06:40.042048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 10122
 
2.3%
0.2 8515
 
1.9%
0.4 8264
 
1.9%
0.3 7820
 
1.8%
0.5 7614
 
1.7%
0.6 7364
 
1.7%
0.7 6907
 
1.5%
0.8 6206
 
1.4%
0.9 5278
 
1.2%
1 4599
 
1.0%
Other values (90) 28469
 
6.4%
(Missing) 344856
77.3%
ValueCountFrequency (%)
0 89
 
< 0.1%
0.1 10122
2.3%
0.2 8515
1.9%
0.3 7820
1.8%
0.4 8264
1.9%
0.5 7614
1.7%
0.6 7364
1.7%
0.7 6907
1.5%
0.8 6206
1.4%
0.9 5278
1.2%
ValueCountFrequency (%)
25.1 1
< 0.1%
15.5 1
< 0.1%
13.1 1
< 0.1%
12.1 1
< 0.1%
11.1 1
< 0.1%
9.9 1
< 0.1%
9.6 1
< 0.1%
9.5 2
< 0.1%
9.4 2
< 0.1%
9 1
< 0.1%

NO (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct360
Distinct (%)0.1%
Missing30984
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean13.225808
Minimum-441
Maximum634
Zeros3472
Zeros (%)0.8%
Negative3
Negative (%)< 0.1%
Memory size3.4 MiB
2025-01-23T20:06:40.098710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-441
5-th percentile1
Q12
median5
Q315
95-th percentile52
Maximum634
Range1075
Interquartile range (IQR)13

Descriptive statistics

Standard deviation21.970729
Coefficient of variation (CV)1.6612013
Kurtosis39.722907
Mean13.225808
Median Absolute Deviation (MAD)4
Skewness4.6985519
Sum5489107
Variance482.71295
MonotonicityNot monotonic
2025-01-23T20:06:40.158544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 67032
15.0%
2 54774
 
12.3%
3 35928
 
8.1%
4 26810
 
6.0%
5 21890
 
4.9%
6 17691
 
4.0%
7 15166
 
3.4%
8 13163
 
3.0%
9 11770
 
2.6%
10 9851
 
2.2%
Other values (350) 140955
31.6%
(Missing) 30984
 
6.9%
ValueCountFrequency (%)
-441 1
 
< 0.1%
-374 1
 
< 0.1%
-367 1
 
< 0.1%
0 3472
 
0.8%
1 67032
15.0%
2 54774
12.3%
3 35928
8.1%
4 26810
 
6.0%
5 21890
 
4.9%
6 17691
 
4.0%
ValueCountFrequency (%)
634 1
< 0.1%
610 1
< 0.1%
511 1
< 0.1%
470 1
< 0.1%
464 1
< 0.1%
434 1
< 0.1%
419 1
< 0.1%
418 1
< 0.1%
408 1
< 0.1%
406 1
< 0.1%

NO2 (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct207
Distinct (%)0.1%
Missing32517
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean21.409154
Minimum0
Maximum249
Zeros138
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-01-23T20:06:40.218256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median16
Q329
95-th percentile59
Maximum249
Range249
Interquartile range (IQR)21

Descriptive statistics

Standard deviation19.108434
Coefficient of variation (CV)0.89253569
Kurtosis5.8664319
Mean21.409154
Median Absolute Deviation (MAD)10
Skewness1.9289765
Sum8852621
Variance365.13225
MonotonicityNot monotonic
2025-01-23T20:06:40.280732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 15082
 
3.4%
2 14632
 
3.3%
5 14586
 
3.3%
6 14563
 
3.3%
3 14422
 
3.2%
7 14359
 
3.2%
8 13873
 
3.1%
9 13492
 
3.0%
10 13176
 
3.0%
11 12917
 
2.9%
Other values (197) 272395
61.1%
(Missing) 32517
 
7.3%
ValueCountFrequency (%)
0 138
 
< 0.1%
1 11829
2.7%
2 14632
3.3%
3 14422
3.2%
4 15082
3.4%
5 14586
3.3%
6 14563
3.3%
7 14359
3.2%
8 13873
3.1%
9 13492
3.0%
ValueCountFrequency (%)
249 1
< 0.1%
238 1
< 0.1%
228 1
< 0.1%
222 1
< 0.1%
221 1
< 0.1%
214 1
< 0.1%
211 1
< 0.1%
210 1
< 0.1%
207 1
< 0.1%
204 1
< 0.1%

O3 (ug/m3)
Real number (ℝ)

Missing 

Distinct185
Distinct (%)0.1%
Missing170600
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean52.619754
Minimum0
Maximum999
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-01-23T20:06:40.338712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q137
median54
Q368
95-th percentile87
Maximum999
Range999
Interquartile range (IQR)31

Descriptive statistics

Standard deviation23.221958
Coefficient of variation (CV)0.44131635
Kurtosis128.24065
Mean52.619754
Median Absolute Deviation (MAD)15
Skewness3.7983809
Sum14492217
Variance539.25934
MonotonicityNot monotonic
2025-01-23T20:06:40.395526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 5111
 
1.1%
61 5094
 
1.1%
57 5038
 
1.1%
59 4994
 
1.1%
56 4922
 
1.1%
62 4906
 
1.1%
58 4889
 
1.1%
63 4873
 
1.1%
54 4844
 
1.1%
55 4813
 
1.1%
Other values (175) 225930
50.7%
(Missing) 170600
38.2%
ValueCountFrequency (%)
0 7
 
< 0.1%
1 223
 
< 0.1%
2 290
 
0.1%
3 485
0.1%
4 638
0.1%
5 779
0.2%
6 823
0.2%
7 931
0.2%
8 1014
0.2%
9 1136
0.3%
ValueCountFrequency (%)
999 1
< 0.1%
991 1
< 0.1%
976 1
< 0.1%
965 1
< 0.1%
934 1
< 0.1%
903 1
< 0.1%
899 1
< 0.1%
892 1
< 0.1%
881 1
< 0.1%
871 1
< 0.1%

PM10 (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct237
Distinct (%)0.1%
Missing101435
Missing (%)22.7%
Infinite0
Infinite (%)0.0%
Mean22.694662
Minimum0
Maximum557
Zeros107
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-01-23T20:06:40.602855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median18
Q329
95-th percentile57
Maximum557
Range557
Interquartile range (IQR)18

Descriptive statistics

Standard deviation17.919319
Coefficient of variation (CV)0.789583
Kurtosis13.698174
Mean22.694662
Median Absolute Deviation (MAD)8
Skewness2.4617604
Sum7820104
Variance321.10201
MonotonicityNot monotonic
2025-01-23T20:06:40.662023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 14349
 
3.2%
10 14264
 
3.2%
8 13778
 
3.1%
11 13618
 
3.1%
12 13314
 
3.0%
13 12752
 
2.9%
14 12569
 
2.8%
7 12265
 
2.7%
15 12087
 
2.7%
16 11473
 
2.6%
Other values (227) 214110
48.0%
(Missing) 101435
22.7%
ValueCountFrequency (%)
0 107
 
< 0.1%
1 1284
 
0.3%
2 2665
 
0.6%
3 3471
 
0.8%
4 5075
 
1.1%
5 7281
1.6%
6 9813
2.2%
7 12265
2.7%
8 13778
3.1%
9 14349
3.2%
ValueCountFrequency (%)
557 1
< 0.1%
343 1
< 0.1%
339 1
< 0.1%
321 2
< 0.1%
314 1
< 0.1%
292 1
< 0.1%
290 1
< 0.1%
289 1
< 0.1%
284 1
< 0.1%
252 1
< 0.1%

PM25 (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct177
Distinct (%)0.3%
Missing392230
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean13.677172
Minimum0
Maximum223
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-01-23T20:06:40.719227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median9
Q315
95-th percentile46
Maximum223
Range223
Interquartile range (IQR)10

Descriptive statistics

Standard deviation15.895495
Coefficient of variation (CV)1.1621916
Kurtosis18.879228
Mean13.677172
Median Absolute Deviation (MAD)5
Skewness3.5589683
Sum735613
Variance252.66675
MonotonicityNot monotonic
2025-01-23T20:06:40.776940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3727
 
0.8%
6 3723
 
0.8%
4 3662
 
0.8%
7 3512
 
0.8%
3 3409
 
0.8%
8 3376
 
0.8%
9 3183
 
0.7%
2 2924
 
0.7%
10 2722
 
0.6%
11 2546
 
0.6%
Other values (167) 21000
 
4.7%
(Missing) 392230
87.9%
ValueCountFrequency (%)
0 9
 
< 0.1%
1 1178
 
0.3%
2 2924
0.7%
3 3409
0.8%
4 3662
0.8%
5 3727
0.8%
6 3723
0.8%
7 3512
0.8%
8 3376
0.8%
9 3183
0.7%
ValueCountFrequency (%)
223 1
< 0.1%
218 1
< 0.1%
216 1
< 0.1%
205 1
< 0.1%
196 2
< 0.1%
193 1
< 0.1%
188 1
< 0.1%
186 1
< 0.1%
183 1
< 0.1%
179 1
< 0.1%

SO2 (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct251
Distinct (%)0.1%
Missing89737
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean9.0928014
Minimum-791
Maximum364
Zeros1116
Zeros (%)0.3%
Negative41
Negative (%)< 0.1%
Memory size3.4 MiB
2025-01-23T20:06:40.833151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-791
5-th percentile1
Q12
median5
Q311
95-th percentile31
Maximum364
Range1155
Interquartile range (IQR)9

Descriptive statistics

Standard deviation13.79075
Coefficient of variation (CV)1.5166668
Kurtosis338.90028
Mean9.0928014
Median Absolute Deviation (MAD)3
Skewness-2.5880381
Sum3239556
Variance190.18477
MonotonicityNot monotonic
2025-01-23T20:06:40.892166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 46443
10.4%
1 44183
9.9%
3 38786
8.7%
4 32839
 
7.4%
5 26597
 
6.0%
6 21112
 
4.7%
7 17852
 
4.0%
8 14822
 
3.3%
9 12354
 
2.8%
10 10323
 
2.3%
Other values (241) 90966
20.4%
(Missing) 89737
20.1%
ValueCountFrequency (%)
-791 1
 
< 0.1%
-783 1
 
< 0.1%
-780 1
 
< 0.1%
-773 3
< 0.1%
-767 1
 
< 0.1%
-763 2
< 0.1%
-399 1
 
< 0.1%
-393 2
< 0.1%
-388 1
 
< 0.1%
-387 1
 
< 0.1%
ValueCountFrequency (%)
364 1
< 0.1%
360 1
< 0.1%
358 1
< 0.1%
344 1
< 0.1%
326 1
< 0.1%
320 1
< 0.1%
270 1
< 0.1%
261 1
< 0.1%
250 1
< 0.1%
248 1
< 0.1%

Provincia
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size436.1 KiB
León
176821 
Valladolid
80460 
Palencia
61857 
Burgos
57906 
Salamanca
31204 
Other values (5)
37766 

Length

Max length10
Median length9
Mean length6.3943778
Min length4

Characters and Unicode

Total characters2851982
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBurgos
2nd rowLeón
3rd rowLeón
4th rowLeón
5th rowLeón

Common Values

ValueCountFrequency (%)
León 176821
39.6%
Valladolid 80460
18.0%
Palencia 61857
 
13.9%
Burgos 57906
 
13.0%
Salamanca 31204
 
7.0%
Soria 10482
 
2.4%
Zamora 8398
 
1.9%
Segovia 8245
 
1.8%
Avila 7389
 
1.7%
Madrid 3252
 
0.7%

Length

2025-01-23T20:06:40.948787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-23T20:06:41.000006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
león 176821
39.6%
valladolid 80460
18.0%
palencia 61857
 
13.9%
burgos 57906
 
13.0%
salamanca 31204
 
7.0%
soria 10482
 
2.4%
zamora 8398
 
1.9%
segovia 8245
 
1.8%
avila 7389
 
1.7%
madrid 3252
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 455614
16.0%
l 341830
12.0%
n 269882
9.5%
e 246923
8.7%
L 176821
 
6.2%
ó 176821
 
6.2%
i 171685
 
6.0%
d 167424
 
5.9%
o 165491
 
5.8%
c 93061
 
3.3%
Other values (13) 586430
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2851982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 455614
16.0%
l 341830
12.0%
n 269882
9.5%
e 246923
8.7%
L 176821
 
6.2%
ó 176821
 
6.2%
i 171685
 
6.0%
d 167424
 
5.9%
o 165491
 
5.8%
c 93061
 
3.3%
Other values (13) 586430
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2851982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 455614
16.0%
l 341830
12.0%
n 269882
9.5%
e 246923
8.7%
L 176821
 
6.2%
ó 176821
 
6.2%
i 171685
 
6.0%
d 167424
 
5.9%
o 165491
 
5.8%
c 93061
 
3.3%
Other values (13) 586430
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2851982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 455614
16.0%
l 341830
12.0%
n 269882
9.5%
e 246923
8.7%
L 176821
 
6.2%
ó 176821
 
6.2%
i 171685
 
6.0%
d 167424
 
5.9%
o 165491
 
5.8%
c 93061
 
3.3%
Other values (13) 586430
20.6%

Estación
Categorical

High cardinality  High correlation 

Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size438.5 KiB
Guardo
 
8644
Leon1
 
8630
La Robla
 
8577
Burgos4
 
8532
Miranda de Ebro2
 
8521
Other values (86)
403110 

Length

Max length32
Median length25
Mean length14.246275
Min length3

Characters and Unicode

Total characters6354038
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBurgos1
2nd rowC.T.L.R. - Naredo
3rd rowCarracedelo
4th rowLa Robla
5th rowTudela Veguin-Tudela Veguin

Common Values

ValueCountFrequency (%)
Guardo 8644
 
1.9%
Leon1 8630
 
1.9%
La Robla 8577
 
1.9%
Burgos4 8532
 
1.9%
Miranda de Ebro2 8521
 
1.9%
Medina del Campo 8503
 
1.9%
Miranda de Ebro1 8224
 
1.8%
Renault3 8154
 
1.8%
Renault2 8095
 
1.8%
Renault4 8082
 
1.8%
Other values (81) 362052
81.2%

Length

2025-01-23T20:06:41.065829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
82839
 
9.1%
de 60808
 
6.7%
c.t.anllares 35105
 
3.9%
del 26156
 
2.9%
c.t.l.r 22403
 
2.5%
la 20978
 
2.3%
miranda 19207
 
2.1%
2 17772
 
2.0%
ii 17516
 
1.9%
c.t.g 14250
 
1.6%
Other values (112) 592242
65.1%

Most occurring characters

ValueCountFrequency (%)
a 703230
 
11.1%
l 510181
 
8.0%
463262
 
7.3%
o 457350
 
7.2%
e 422344
 
6.6%
r 328159
 
5.2%
n 298781
 
4.7%
i 298635
 
4.7%
. 296522
 
4.7%
C 226115
 
3.6%
Other values (43) 2349459
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6354038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 703230
 
11.1%
l 510181
 
8.0%
463262
 
7.3%
o 457350
 
7.2%
e 422344
 
6.6%
r 328159
 
5.2%
n 298781
 
4.7%
i 298635
 
4.7%
. 296522
 
4.7%
C 226115
 
3.6%
Other values (43) 2349459
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6354038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 703230
 
11.1%
l 510181
 
8.0%
463262
 
7.3%
o 457350
 
7.2%
e 422344
 
6.6%
r 328159
 
5.2%
n 298781
 
4.7%
i 298635
 
4.7%
. 296522
 
4.7%
C 226115
 
3.6%
Other values (43) 2349459
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6354038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 703230
 
11.1%
l 510181
 
8.0%
463262
 
7.3%
o 457350
 
7.2%
e 422344
 
6.6%
r 328159
 
5.2%
n 298781
 
4.7%
i 298635
 
4.7%
. 296522
 
4.7%
C 226115
 
3.6%
Other values (43) 2349459
37.0%

Latitud
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)< 0.1%
Missing226
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean42.151547
Minimum38.938333
Maximum43.603333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-01-23T20:06:41.117651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum38.938333
5-th percentile40.949722
Q141.645556
median42.542778
Q342.688056
95-th percentile42.849167
Maximum43.603333
Range4.665
Interquartile range (IQR)1.0425

Descriptive statistics

Standard deviation0.66550058
Coefficient of variation (CV)0.015788284
Kurtosis-0.59538138
Mean42.151547
Median Absolute Deviation (MAD)0.30638889
Skewness-0.6832054
Sum18790654
Variance0.44289103
MonotonicityNot monotonic
2025-01-23T20:06:41.176230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.94972222 10177
 
2.3%
41.67111111 8654
 
1.9%
41.31638889 8503
 
1.9%
41.61277778 8154
 
1.8%
41.60416667 8095
 
1.8%
41.96138889 8082
 
1.8%
41.6 7922
 
1.8%
42.79527778 7913
 
1.8%
42.60388889 7900
 
1.8%
42.80277778 7847
 
1.8%
Other values (91) 362541
81.3%
ValueCountFrequency (%)
38.93833333 36
 
< 0.1%
40.38694444 3252
 
0.7%
40.56944444 729
 
0.2%
40.57055556 2885
 
0.6%
40.65861111 4253
1.0%
40.66472222 3136
 
0.7%
40.94972222 10177
2.3%
40.95555556 3245
 
0.7%
40.95583333 727
 
0.2%
40.96055556 729
 
0.2%
ValueCountFrequency (%)
43.60333333 730
 
0.2%
43.04166667 708
 
0.2%
43.04111111 3038
0.7%
42.9525 3102
0.7%
42.95166667 716
 
0.2%
42.94416667 6667
1.5%
42.87777778 5547
1.2%
42.84916667 7211
1.6%
42.84638889 6225
1.4%
42.8425 2342
 
0.5%

Longitud
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)< 0.1%
Missing226
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-5.1789653
Minimum-6.7819444
Maximum-2.4666667
Zeros0
Zeros (%)0.0%
Negative445788
Negative (%)99.9%
Memory size3.4 MiB
2025-01-23T20:06:41.233048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6.7819444
5-th percentile-6.6622222
Q1-6.4838889
median-4.9091667
Q3-4.5383333
95-th percentile-2.9405556
Maximum-2.4666667
Range4.3152778
Interquartile range (IQR)1.9455556

Descriptive statistics

Standard deviation1.1219204
Coefficient of variation (CV)-0.21663022
Kurtosis-0.62728166
Mean-5.1789653
Median Absolute Deviation (MAD)0.75277778
Skewness0.34897154
Sum-2308720.6
Variance1.2587053
MonotonicityNot monotonic
2025-01-23T20:06:41.286730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.725555556 12407
 
2.8%
-6.520833333 11157
 
2.5%
-3.683888889 8654
 
1.9%
-4.909166667 8503
 
1.9%
-4.740833333 8154
 
1.8%
-4.728888889 8095
 
1.8%
-4.494444444 8082
 
1.8%
-4.7325 7922
 
1.8%
-4.840833333 7913
 
1.8%
-5.587222222 7900
 
1.8%
Other values (90) 357001
80.0%
ValueCountFrequency (%)
-6.781944444 6257
1.4%
-6.725555556 12407
2.8%
-6.662222222 4555
 
1.0%
-6.653611111 4565
 
1.0%
-6.643333333 6717
1.5%
-6.625 1812
 
0.4%
-6.603888889 5887
1.3%
-6.600277778 4609
 
1.0%
-6.589444444 6647
1.5%
-6.588888889 2501
 
0.6%
ValueCountFrequency (%)
-2.466666667 5856
1.3%
-2.480555556 731
 
0.2%
-2.856944444 3895
0.9%
-2.9175 7742
1.7%
-2.918055556 482
 
0.1%
-2.940277778 731
 
0.2%
-2.940555556 7790
1.7%
-2.954444444 2462
 
0.6%
-3.475277778 3818
0.9%
-3.636111111 7806
1.8%
Distinct106
Distinct (%)< 0.1%
Missing226
Missing (%)0.1%
Memory size3.4 MiB
2025-01-23T20:06:41.432759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length28
Mean length26.7056
Min length12

Characters and Unicode

Total characters11905036
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42.3511111111,-3.67555555556
2nd row42.8166666667,-5.53333333333
3rd row42.5586111111,-6.72555555556
4th row42.8016666667,-5.625
5th row42.8016666667,-5.64888888889
ValueCountFrequency (%)
41.6711111111,-3.68388888889 8654
 
1.9%
41.3163888889,-4.90916666667 8503
 
1.9%
41.6127777778,-4.74083333333 8154
 
1.8%
41.6041666667,-4.72888888889 8095
 
1.8%
41.9613888889,-4.49444444444 8082
 
1.8%
41.6,-4.7325 7922
 
1.8%
42.7952777778,-4.84083333333 7913
 
1.8%
42.6038888889,-5.58722222222 7900
 
1.8%
42.8027777778,-5.62361111111 7847
 
1.8%
42.3361111111,-3.63611111111 7806
 
1.8%
Other values (96) 364912
81.9%
2025-01-23T20:06:41.632920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 1527192
12.8%
8 1324244
11.1%
6 1243180
10.4%
5 1178927
9.9%
3 1135738
9.5%
1 1116551
9.4%
7 994101
8.4%
2 950239
8.0%
. 891576
7.5%
, 445788
 
3.7%
Other values (3) 1097500
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11905036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 1527192
12.8%
8 1324244
11.1%
6 1243180
10.4%
5 1178927
9.9%
3 1135738
9.5%
1 1116551
9.4%
7 994101
8.4%
2 950239
8.0%
. 891576
7.5%
, 445788
 
3.7%
Other values (3) 1097500
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11905036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 1527192
12.8%
8 1324244
11.1%
6 1243180
10.4%
5 1178927
9.9%
3 1135738
9.5%
1 1116551
9.4%
7 994101
8.4%
2 950239
8.0%
. 891576
7.5%
, 445788
 
3.7%
Other values (3) 1097500
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11905036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 1527192
12.8%
8 1324244
11.1%
6 1243180
10.4%
5 1178927
9.9%
3 1135738
9.5%
1 1116551
9.4%
7 994101
8.4%
2 950239
8.0%
. 891576
7.5%
, 445788
 
3.7%
Other values (3) 1097500
9.2%

Interactions

2025-01-23T20:06:37.954713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:33.664047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.133438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.793726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.333594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.823878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.337768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.758057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.406759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:38.023567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:33.723145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.198494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.860146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.389556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.885593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.383645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.944190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.476687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:38.086071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:33.782076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.272009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.926156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.448007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.948216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.431273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.005945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.542848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:38.143138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:33.830316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.335061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.984835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.501615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.002976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.477252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.065958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.600646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:38.204902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:33.879625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.395465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.045124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.556002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.063076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.525500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.124799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.662803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:38.251980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:33.925103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.553585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.090063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.602686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.111201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.570559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.173371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.708724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:38.316770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:33.973562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.614767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.151386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.659341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.166687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.614022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.229470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.772932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:38.390205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.028760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.681599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.219498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.718502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.232174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.658688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.293810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.839937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:38.462387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.084741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:34.745857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.286013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:35.777554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.294396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:36.708292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.356225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-23T20:06:37.905785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-23T20:06:41.685241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CO (mg/m3)EstaciónLatitudLongitudNO (ug/m3)NO2 (ug/m3)O3 (ug/m3)PM10 (ug/m3)PM25 (ug/m3)ProvinciaSO2 (ug/m3)
CO (mg/m3)1.0000.230-0.043-0.0690.6050.591-0.2470.5850.6980.0990.649
Estación0.2301.0000.8650.9990.2070.3180.2240.1290.2771.0000.183
Latitud-0.0430.8651.000-0.300-0.225-0.229-0.038-0.232-0.4540.6640.065
Longitud-0.0690.999-0.3001.0000.1340.1120.0460.223-0.0750.731-0.078
NO (ug/m3)0.6050.207-0.2250.1341.0000.782-0.4740.5760.6340.0570.551
NO2 (ug/m3)0.5910.318-0.2290.1120.7821.000-0.4470.5730.6780.0950.507
O3 (ug/m3)-0.2470.224-0.0380.046-0.474-0.4471.000-0.154-0.2540.030-0.252
PM10 (ug/m3)0.5850.129-0.2320.2230.5760.573-0.1541.0000.8580.0520.412
PM25 (ug/m3)0.6980.277-0.454-0.0750.6340.678-0.2540.8581.0000.3290.487
Provincia0.0991.0000.6640.7310.0570.0950.0300.0520.3291.0000.063
SO2 (ug/m3)0.6490.1830.065-0.0780.5510.507-0.2520.4120.4870.0631.000

Missing values

2025-01-23T20:06:38.544205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-23T20:06:38.805221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-23T20:06:39.660998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FechaCO (mg/m3)NO (ug/m3)NO2 (ug/m3)O3 (ug/m3)PM10 (ug/m3)PM25 (ug/m3)SO2 (ug/m3)ProvinciaEstaciónLatitudLongitudPosición
02020-12-310.68.016.0NaN6.0NaN1.0BurgosBurgos142.351111-3.67555642.3511111111,-3.67555555556
12020-12-31NaN2.06.0NaN8.0NaN4.0LeónC.T.L.R. - Naredo42.816667-5.53333342.8166666667,-5.53333333333
22020-12-31NaN8.02.065.04.0NaN2.0LeónCarracedelo42.558611-6.72555642.5586111111,-6.72555555556
32020-12-31NaN1.04.058.021.0NaN17.0LeónLa Robla42.801667-5.62500042.8016666667,-5.625
42020-12-31NaN1.01.0NaN6.0NaN2.0LeónTudela Veguin-Tudela Veguin42.801667-5.64888942.8016666667,-5.64888888889
52020-12-31NaN2.02.062.06.0NaN6.0LeónValderas42.079722-5.44888942.0797222222,-5.44888888889
62020-12-31NaN5.09.056.05.0NaN5.0PalenciaGuardo42.791389-4.84638942.7913888889,-4.84638888889
72020-12-31NaN2.06.053.06.0NaN5.0PalenciaHontoria 1 - Poblado41.932778-4.47000041.9327777778,-4.47
82020-12-31NaN2.06.059.05.0NaNNaNPalenciaRenault441.961389-4.49444441.9613888889,-4.49444444444
92020-12-31NaN1.01.081.0NaNNaN2.0SalamancaEl Maillo40.569444-6.22388940.5694444444,-6.22388888889
FechaCO (mg/m3)NO (ug/m3)NO2 (ug/m3)O3 (ug/m3)PM10 (ug/m3)PM25 (ug/m3)SO2 (ug/m3)ProvinciaEstaciónLatitudLongitudPosición
4460041997-01-012.432.048.052.0NaNNaN17.0PalenciaPalencia242.003611-4.52472242.0036111111,-4.52472222222
4460051997-01-01NaN10.015.057.051.0NaN23.0PalenciaVelilla del Rio Carrion42.828056-4.84305642.8280555556,-4.84305555556
4460061997-01-011.523.037.054.0NaNNaN14.0SalamancaSalamanca440.949722-5.65833340.9497222222,-5.65833333333
4460071997-01-011.123.043.0291.052.0NaN6.0BurgosBurgos142.350833-3.67555642.3508333333,-3.67555555556
4460081997-01-011.243.054.029.0124.0NaN32.0LeónLeon142.603889-5.58722242.6038888889,-5.58722222222
4460091997-01-01NaN31.044.09.036.0NaN37.0LeónLeon242.588611-5.57138942.5886111111,-5.57138888889
4460101997-01-01NaN9.018.032.035.0NaN40.0PalenciaGuardo42.795278-4.84083342.7952777778,-4.84083333333
4460111997-01-011.513.031.032.045.0NaN8.0BurgosAranda de Duero41.671111-3.68388941.6711111111,-3.68388888889
4460121997-01-012.017.054.086.032.0NaN13.0SegoviaSegovia40.949722-4.11583340.9497222222,-4.11583333333
4460131997-01-014.8122.086.0NaNNaN72.0110.0SalamancaSalamanca140.973611-5.66277840.9736111111,-5.66277777778